Hip joint centre localisation with an unscented Kalman filter

Momi, Elena De; Beretta, Elisa; Ferrigno, Giancarlo

doi:10.1080/10255842.2012.670852

Cited by 5 publications

(12 citation statements)

References 59 publications

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“…The HJC position in the laboratory space can be tracked under the hypothesis of motion regularity according to the Bayesian approach. Unlike the previously proposed UKF method, 21 the dual estimation approach provides the estimation of the system state and of the parameter vectors in two parallel and intertwined filtering processes, allowing the adaptation of the parameter filter gains through a simulated annealing algorithm. The computational time (in the range of 15# and 45# for a signal 1000 frames in length, using MatLab R2009b for computation, on a dual core central processing unit (CPU) at 3 GHz), due to the Kalman joint estimation and simulated annealing, limits the immediate clinical applicability of the approach.…”

Section: Discussionmentioning

confidence: 99%

“…21 As shown in Figure 1, the description of the kinematic chain requires the definition of the following:…”

Section: The Hip Kinematic Modelmentioning

confidence: 99%

“…PIV: the pivoting method, 19 using the instantaneous TF F poses; 3. UKF: the unscented Kalman filter method, 21 using the instantaneous TF F poses and the positions of the simulated pelvic point A expressed in the TF L ; 4. dUKF: the dual unscented Kalman filter method, using the instantaneous TF F poses and the positions of the simulated pelvic point A expressed in the TF L .…”

Section: Algorithm Validationmentioning

confidence: 99%

“…Recently, De Momi et al 21 proposed the application of a skin marker on the pelvis of the subject in order to use the partial information on the hip-bone displacements during the pivoting manoeuvres, without increasing surgical intervention invasiveness. This functional HJC localization method used the unscented Kalman filter (UKF) based on the joint-estimation filtering approach.…”

Section: Introductionmentioning

confidence: 99%

“…19 Both methods use only the femoral poses as input. Additionally, the performance of the UKF algorithm, 21 which uses both the femoral poses and the positions of the pelvic point, was assessed. The HJC position was further estimated as the centre of the best fitting sphere described by the trajectory of the pelvic point in the femoral technical frame.…”

Section: Introductionmentioning

confidence: 99%

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Hip joint centre position estimation using a dual unscented Kalman filter for computer-assisted orthopaedic surgery

Beretta

Momi

Camomilla

et al. 2014

Proc Inst Mech Eng H

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In computer-assisted knee surgery, the accuracy of the localization of the femur centre of rotation relative to the hipbone (hip joint centre) is affected by the unavoidable and untracked pelvic movements because only the femoral pose is acquired during passive pivoting manoeuvres. We present a dual unscented Kalman filter algorithm that allows the estimation of the hip joint centre also using as input the position of a pelvic reference point that can be acquired with a skin marker placed on the hip, without increasing the invasiveness of the surgical procedure. A comparative assessment of the algorithm was carried out using data provided by in vitro experiments mimicking in vivo surgical conditions. Soft tissue artefacts were simulated and superimposed onto the position of a pelvic landmark. Femoral pivoting made of a sequence of star-like quasi-planar movements followed by a circumduction was performed. The dual unscented Kalman filter method proved to be less sensitive to pelvic displacements, which were shown to be larger during the manoeuvres in which the femur was more adducted. Comparable accuracy between all the analysed methods resulted for hip joint centre displacements smaller than 1 mm (error: 2.2 6 [0.2; 0.3] mm, median 6 [inter-quartile range 25%; inter-quartile range 75%]) and between 1 and 6 mm (error: 4.8 6 [0.5; 0.8] mm) during planar movements. When the hip joint centre displacement exceeded 6 mm, the dual unscented Kalman filter proved to be more accurate than the other methods by 30% during multi-planar movements (error: 5.2 6 [1.2; 1] mm).

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Section: Discussionmentioning

confidence: 99%

“…21 As shown in Figure 1, the description of the kinematic chain requires the definition of the following:…”

Section: The Hip Kinematic Modelmentioning

confidence: 99%

Section: Algorithm Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hip joint centre position estimation using a dual unscented Kalman filter for computer-assisted orthopaedic surgery

Beretta

Momi

Camomilla

et al. 2014

Proc Inst Mech Eng H

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Gaussian mixture models based 2D–3D registration of bone shapes for orthopedic surgery planning

Valenti

Ferrigno

Martina

et al. 2016

Med Biol Eng Comput

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In orthopedic surgery, precise kinematics assessment helps the diagnosis and the planning of the intervention. The correct placement of the prosthetic component in the case of knee replacement is necessary to ensure a correct load distribution and to avoid revision of the implant. 3D reconstruction of the knee kinematics under weight-bearing conditions becomes fundamental to understand existing in vivo loads and improve the joint motion tracking. Existing methods rely on the semiautomatic positioning of a shape previously segmented from a CT or MRI on a sequence of fluoroscopic images acquired during knee flexion. We propose a method based on statistical shape models (SSM) automatically superimposed on a sequence of fluoroscopic datasets. Our method is based on Gaussian mixture models, and the core of the algorithm is the maximization of the likelihood of the association between the projected silhouette and the extracted contour from the fluoroscopy image. We evaluated the algorithm using digitally reconstructed radiographies of both healthy and diseased subjects, with a CT-extracted shape and a SSM as the 3D model. In vivo tests were done with fluoroscopically acquired images and subject-specific CT shapes. The results obtained are in line with the literature, but the computational time is substantially reduced.

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Fluoroscopy-based tracking of femoral kinematics with statistical shape models

Valenti

Momi

et al. 2015

Int J CARS

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Abstract:Purpose Precise knee kinematics assessment helps to diagnose knee pathologies and to improve the design of customized prosthetic components. The first step in identifying knee kinematics is to assess the femoral motion in the anatomical frame. However, no work has been done on pathological femurs, whose shape can be highly different from healthy ones. Method We propose a new femoral tracking technique based on Statistical Shape Models (SSMs) and two calibrated fluoroscopic images, taken at different flexion-extension angles. The cost function optimization is based on genetic algorithms, to avoid local minima. The proposed approach was evaluated on 3 sets of digitally reconstructed radiographic images of osteoarthritic patients. Results It is found that using the estimated shape, rather than that calculated from CT, significantly reduces the pose accuracy, but still has reasonably good results (angle errors around 2 degrees, translation around 1.5mm). Cover Letter. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationThe authors thank the reviewers and the editor for their interest in this topic and for giving them the possibility to improve the paper with their comments. Hereunder we answer the questions arisen, with indications on the changes made to the manuscript According to the reviewer comment, we modified the text in the "State of the Art" section as follows:"Knee kinematics assessment has great importance both to understand the problems associated with a large number of knee pathologies and to improve the design of prosthetic components. In case of severe osteoarthritis, that are eligible for joint implant surgery, in vivo pre-operatory knee kinematics is fundamental to understand the relative motion between the three joint bones. The relative movement can give an insight of how the ligaments are stretched and their stability, and how could be the feeling for the patient. In this way, pre operatory knee kinematics could help the surgeon to decide which prosthesis should be used and how to correct the misalignment of the bones [3]. Currently, the reconstruction of the pose of the knee can be done using 3D scans such as real-time Magnetic Resonance Imaging (MRI) or through a 2D/3D registration method that superimposes the shape extracted from MRI or Computed Tomography (CT) onto an image, usually X-ray or fluoroscopy. Real-time MRI is suitable to study joint kinematics, as it evidences the muscle structure during movements. However, it can only be used with relatively slow movements, and the accuracy obtained increases from 1mm to more than 3mm depending on the velocity of the movement. In addition, MRI scans are highly expensive [4]. Traditional CT and MRI provide an accurate evaluation of the morphology of the knee, but are limited to static positioning of the patient.[…]The use of dynamic fluoroscopy to detect knee kinematics is described in [12]. The authors use a fluoroscopic system flashing at 30Hz, obtaining continuous images of the knee flexion from 0° to 120°...

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Hip joint centre localisation with an unscented Kalman filter

Cited by 5 publications

References 59 publications

Hip joint centre position estimation using a dual unscented Kalman filter for computer-assisted orthopaedic surgery

Hip joint centre position estimation using a dual unscented Kalman filter for computer-assisted orthopaedic surgery

Gaussian mixture models based 2D–3D registration of bone shapes for orthopedic surgery planning

Fluoroscopy-based tracking of femoral kinematics with statistical shape models

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